dataset_provenanceThe source, the date it was computed, the licence and the citation for the Ppmly dataset. Read this to attribute a figure correctly.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description carries the burden. It describes the content returned (source, date, licence, citation), making the behavior transparent. Doesn't explicitly state read-only behavior, but it's implicit with a metadata retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that deliver the essential info without fluff. Well-structured and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and no output schema, the description fully covers what the tool does and what it returns. Nothing is missing for a caller to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist (schema coverage 100% with empty schema), so the description doesn't need to elaborate on any. Perfectly clear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the purpose: provides dataset provenance info (source, date, licence, citation). Distinct from sibling tools like dataset_columns or dataset_stats, which handle other aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Read this to attribute a figure correctly', giving a clear when-to-use scenario. Although it doesn't contrast with siblings, the intended use is unmistakable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct purpose: schema, exact lookup, substring search, comparison, stats, top values, and provenance. The 'compare' and 'row' tools could overlap slightly for exact matches, but their descriptions clarify the intended use.
All tools use a consistent 'dataset_' prefix followed by a clear noun or verb, such as dataset_columns, dataset_row, dataset_top. This makes the tool set predictable and easy to navigate.
Seven tools is well within the ideal 3–15 range and covers the essential query operations for a dataset without redundancy. The count feels appropriately scoped for a data exploration server.
The toolkit covers schema, lookup, search, comparison, statistics, ranking, and provenance, which addresses most common dataset questions. A generic 'list all rows' or pagination tool is missing, but the existing tools likely cover typical use cases.